We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the inputs features and therefore more naturally represent data with correlated inputs. CCFs naturally accommodate multiple outputs, provide a similar computational complexity to random forests, and inherit their impressive robustness to the choice of input parameters. As part of the CCF training algorithm, we also introduce projection bootstrapping, a novel alternative to bagging for oblique decision tree ensembles which maintains use of the full dataset in selecting split points, often leading to improvements in predictive accuracy. Our experiments show that, even without parameter tuning, CCFs out-perform axis-aligned random forests and other state-of-the-art tree ensemble methods on both classification and regression problems, delivering both improved predictive accuracy and faster training times. We further show that they outperform all of the 179 classifiers considered in a recent extensive survey.

I have been working on three new AI projects, and am thrilled to announce the first one: deeplearning.ai, These courses will help you master Deep Learning, apply it effectively, and build a career in AI. Just as electricity transformed every major industry starting about 100 years ago, AI is now poised to do the same. Several large tech companies have built AI divisions, and started transforming themselves with AI. But in the next few years, companies of all sizes and across all industries will realize that they too must be part of this AI-powered future.

But it's becoming clear that this type of dystopian view of artificial intelligence is out of phase with humans' real-world expectations and hopes for AI. People know this for two reasons: The first has to do with results of a consumer survey we recently fielded; the second has to do with the power of broccoli (I'll get to that in a moment). Consumers, it turns out, may love dystopian fiction about AI's impact (going all the way back to Mary Shelley's "Frankenstein"), but they firmly believe that, in real life, AI will actually make the world a much better place. That's what we found from an independent global survey we commissioned earlier this year, assessing the public's attitude towards intelligent machines. Not only did it reveal that most people were in favour of AI technology, but they see it as bringing huge potential benefit.

The rise of automated vehicles has provoked a range of ethical and moral discussions, largely revolving around constructs such as the trolley dilemma, which nicely encapsulates the kind of moral decision-making an autonomous vehicle might be forced to make. Historically, it's been considered something that's beyond most autonomous systems. A recent study from The Institute of Cognitive Science at the University of Osnabrück suggests that the kind of moral thinking humans undertake can, in fact, be accurately modeled for autonomous systems to use. The system uses immersive virtual reality to study human behavior in a wide range of simulated road traffic scenarios. For instance, participants were asked to drive in a typical suburban setting in foggy weather.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Stanford University AI researchers have created a "socially equitable" natural language processing (NLP) tool they say improves upon off-the-shelf AI solutions used today that fail to account for things like regional dialects, slang, or the natural way people talk when they regularly speak more than one language. In a paper published late last week, researchers found Equilid to be more accurate than commonly used identification tools like langid.py and Google's CLD2. Popular language identification tools, the paper argues, draw on a "European-centric corpora" of the written word, as well as websites, Wikipedia, and newswires, methods that may not best represent the way people actually talk. Language identification is a form of NLP used for things like serving up Google search results or even tracking social media chatter to make predictions. Equilid was made to better understand slang, regional dialects, and the natural way people communicate online when they speak more than one language, like, say, the 90 million English speakers in the Philippines who may regularly switch between English and Tagalog.

The advent of social media presents a promising new opportunity for early detection and intervention in psychiatric disorders. Predictive screening methods have successfully analyzed online media to detect a number of harmful health conditions [1–11]. All of these studies relied on text analysis, however, and none have yet harnessed the wealth of psychological data encoded in visual social media, such as photographs posted to Instagram. In this report, we introduce a methodology for analyzing photographic data from Instagram to predictively screen for depression. There is good reason to prioritize research into Instagram analysis for health screening.

Deliver lightening fast customer support at scale requires a lot of resources and this is why you want your end users or employee to be able to self serve as much as possible. Bots have been around for a long time, but recent advances in natural language processing drove huge adoption over the last year. They are now being used to enhance a broad set of experiences with customer service being one of the most relevant. In this article we're going to learn what does it take to save time to our customer support team. To do this we'll build a chatbot to automate answers to frequently asked questions, eventually saving precious time to your customer support operators so they can focus on more complex requests.

Over the last few years, AI assistants have been on the rise. First, as a fun feature set, now ingrained as part of our daily lives. The personal brand experience, that is, how you interact with technology and brands is rapidly transforming. But will AI assistants and voice platforms rule the space? What do you think about the voice platform?

It was supposed to be a demonstration of just how much Boston Dynamic's six foot tall humanoid robot has improved. Instead, the latest test of the machine's prowess descended into disaster when it fell off the stage at the Congress of Future Science and Technology Leaders conference in Massachusetts. The fall promoted staff to rush to its aid, and followed a series of previous blunders that saw it trip repeatedly during a recent'Robot Olympics' event. According to Boston Dynamics, Atlas is a'high mobility, humanoid robot designed to negotiate outdoor, rough terrain. 'Atlas can walk bipedally leaving the upper limbs free to lift, carry, and manipulate the environment.